In order to safely navigate traffic, automated vehicles need to face a variety of challenges such as the uncertainty of typical road environments. A key component of road traffic, that both illustrates such uncertainty and highly influences traffic participants behavior, is parked cars. For instance, mistakenly considering a car as parked directly increases the collision risk and, on the other hand, expecting parked cars to move leads to over-precautions, inefficient, and potentially confusing planned trajectories. Despite being a major component of traffic prediction, little work addresses the parked car detection, and to our knowledge none in the context of autonomous driving. This paper introduces the problem of parked car classification and illustrates some of its challenges and corner cases. For generality, we focus on sensor-set independent classification and in particular we ignore vehicle light information which further increases the difficulty of the problem. Our contribution includes introducing, discussing, and comparing a list of features as candidate predictors of parked car classification. More precisely, we collect and annotate 180,469 vehicle occurrences over a duration of approximately 21 minutes; we then compare the classification accuracy of a list of features over a few classification methods. Based on sensor set independent detections and map information, we achieve an accuracy of 92.9% on our test-set and discuss how this can likely be improved.

Research in human teamwork shows that a key element of fluid and fluent interactions is the interpretation of implicit verbal and non-verbal cues in context. This poses an issue to robotic platforms, however, as they have historically worked best when controlled through explicit commands that have employed structured, unequivocal representations of the external world and their human partners. In this work, we present a framework for effectively grounding situated and naturalistic speech to action selection during human-robot collaborative activities. This is accomplished by maintaining and incrementally updating separate “speech” and “context” models that jointly classify a collaborator's utterance. We evaluate the efficacy of the system on a collaborative construction task with an autonomous robot and human participants. We first demonstrate that our system is capable of acquiring and deploying new task representations from limited and naturalistic data sets, and without any prior domain knowledge of language or the task itself. Finally, we show that our system is capable of significantly improving performance on an unfamiliar task after a one-shot exposure.

Human-Robot Collaboration (HRC) aims to develop robots that provide assistance to human workers while performing physical tasks. Such assistance comes in the form of supportive behaviors that are different from the actions part of the task, and that are meant to help a human worker more effectively accomplish the task. Learning how to provide useful behaviors that are tailored to a human peer represents a difficult challenge. This is due to the need of large amounts of training data in the form of real world observations that include information about such preferences. This data needs to encode not only the structure and progression of the task, but also the different workers' preferences with respect to when and what assistance the robot should provide. Our work separates the challenge of learning a model of the task (which requires a large amount of training data) from that of learning supportive behavior preferences for the interaction (which has obvious restrictions for the number of user-provided demonstrations to which we have access). We first learn a hidden Markov model (HMM) from a training set consisting of observed human workers performing the considered task in simulation. We then use this model to predict, while observing the human peer, what supportive behaviors a robot should offer throughout the task. Building upon the hidden state representation, our system is able to learn the supportive behaviors based on as few as five user-annotated demonstrations, learning a personalized supportive behavior model. We evaluate our system on a user study with 14 participants, and show results on par with human-level prediction for the task.

In this paper, we present a model set for designing human-robot collaboration (HRC) experiments. It targets a common scenario in HRC, which is the collaborative assembly of furniture, and it consists of a combination of standard components and custom designs. With this work, we aim at reducing the amount of work required to set up and reproduce HRC experiments, and we provide a unified framework to facilitate the comparison and integration of contributions to the field. The model set is designed to be modular, extendable, and easy to distribute. Importantly, it covers the majority of relevant research in HRC, and it allows tuning of a number of experimental variables that are particularly valuable to the field. Additionally, we provide a set of software libraries for perception, control and interaction, with the goal of encouraging other researchers to proactively contribute to our work.

Collaborative robots represent a clear added value to manufacturing, as they promise to increase productivity and improve working conditions of such environments. Although modern robotic systems have become safe and reliable enough to operate close to human workers on a day-to-day basis, the workload is still skewed in favor of a limited contribution from the robot's side, and a significant cognitive load is allotted to the human. We believe the transition from robots as recipients of human instruction to robots as capable collaborators hinges around the implementation of transparent systems, where mental models about the task are shared between peers, and the human partner is freed from the responsibility of taking care of both actors. In this work, we implement a transparent task planner able to be deployed in realistic, near-future applications. The proposed framework is capable of basic reasoning capabilities for what concerns role assignment and task allocation, and it interfaces with the human partner at the level of abstraction he is most comfortable with. The system is readily available to non-expert users, and programmable with high-level commands in an intuitive interface. Our results demonstrate an overall improvement in terms of completion time, as well as a reduced cognitive load for the human partner.

We present an approach, based on non-negative matrix factorization, for learning to recognize parallel combinations of initially unknown human motion primitives, associated with ambiguous sets of linguistic labels during training. In the training phase, the learner observes a human producing complex motions which are parallel combinations of initially unknown motion primitives. Each time the human shows a complex motion, he also provides high-level linguistic descriptions, consisting of a set of labels giving the name of the primitives inside the complex motion. From the observation of multimodal combinations of high-level labels with high-dimensional continuous unsegmented values representing complex motions, the learner must later on be able to recognize, through the production of the adequate set of labels, which are the motion primitives in a novel complex motion produced by a human, even if those combinations were never observed during training. We explain how this problem, as well as natural extensions, can be addressed using non-negative matrix factorization. Then, we show in an experiment in which a learner has to recognize the primitive motions of complex human dance choreographies, that this technique allows the system to infer with good performance the combinatorial structure of parallel combinations of unknown primitives.